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Breaking Through Silos with Big Data Analytics

One of the biggest challenges many enterprises face is trying to glean insights from big data that’s trapped in the data silos that exist across business units and organizational functions (e.g., contact center, marketing, sales).

Unknown Breaking Through Silos with Big Data AnalyticsThese data vaults prevent decision makers from gaining unified views of customer and operational information.

Moreover, they stymie the efforts of enterprises to leverage the full range of available data that can reveal insights into emerging customer trends or market shifts that can be acted on quickly to optimize business performance.

Data silos create a number of barriers that impede decision making and organizational performance.

“It’s easy to lose track of the real driver behind big data – great and timely business analytics that derive from an end to end picture of business situations decision makers are trying to understand and respond to,” according to an article in TechRepublic.

The use of big data analytics is helping enterprises generate impressive business and operational gains. However, data silos continue to constrain the potential that can be achieved in gaining a 360-degree view of customers as well as a comprehensive picture of business operations.

While organizations that are using big data analytics are seeing an average 26% improvement in performance, respondents are expecting to see a full 41% improvement over the next three years, according to a recent study by Capgemini.

“Data silos are a perennial problem and one which the business process reengineering revolution of the 1990s failed to resolve,” the report notes.

Of course, enterprise IT infrastructures play a significant role as to how well – or poorly – data is integrated across different parts of the business.

“[T]he stubborn presence of data silos . . . prevent analytics engines from gaining a true picture of both structured and unstructured data sets,” according to a recent blog post in IT Business Edge.

For decision makers to make informed decisions, they need the full range of customer, operational, and market information that’s available to them. This includes commonly-used structured data (data contained in customer databases) and unstructured data (email, social media posts, audio, video).

Before embarking on a comprehensive, not to mention costly, data integration program, organizations should test out a pilot program that involves data integration/sharing between two business units or functions within the enterprise such as the contact center and marketing.

The contact center is a rich repository of customer feedback (recorded voice interactions, chat, email, etc.). A data integration pilot should start by identifying a key business challenge (e.g., customer churn in a particular geographic region or among a specific customer segment).

Gathering and then analyzing customer feedback that’s shared in the contact center with marketing can unearth a trove of insights about what customers view as their primary pain points with a company.

Analytics can be used to drill down for a root cause analysis, enabling business leaders to identify the sources of customer dissatisfaction that are leading to churn and then develop an action plan to address the issues.

Project leaders can then display what they’ve learned and achieved through the data-sharing exercise to demonstrate the business value of data integration to senior management. Taking a tactical approach to data integration can help strengthen the business case for more comprehensive data integration investments.

Next Steps:

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